hands-on exercise 1

Author

XU LIN

##Overview

This is the overview paragraph.

##Getting Started

This is the getting start paragraph

pacman::p_load(sf, spdep, tmap, tidyverse, knitr)
mpsz <- st_read(dsn = "data/geospatial", layer = "MP14_SUBZONE_WEB_PL")
Reading layer `MP14_SUBZONE_WEB_PL' from data source 
  `/Users/linxu/ISSS624/hands-on exercise 1/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21
cyclingpath = st_read(dsn = "data/geospatial",                           layer = "CyclingPathGazette")
Reading layer `CyclingPathGazette' from data source 
  `/Users/linxu/ISSS624/hands-on exercise 1/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 2558 features and 2 fields
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: 11854.32 ymin: 28347.98 xmax: 42626.09 ymax: 48948.15
Projected CRS: SVY21
preschool = st_read("data/geospatial/PreSchoolsLocation.kml")
Reading layer `PRESCHOOLS_LOCATION' from data source 
  `/Users/linxu/ISSS624/hands-on exercise 1/data/geospatial/PreSchoolsLocation.kml' 
  using driver `KML'
Simple feature collection with 2290 features and 2 fields
Geometry type: POINT
Dimension:     XYZ
Bounding box:  xmin: 103.6878 ymin: 1.247759 xmax: 103.9897 ymax: 1.462134
z_range:       zmin: 0 zmax: 0
Geodetic CRS:  WGS 84
st_geometry(mpsz)
Geometry set for 323 features 
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21
First 5 geometries:
MULTIPOLYGON (((31495.56 30140.01, 31980.96 296...
MULTIPOLYGON (((29092.28 30021.89, 29119.64 300...
MULTIPOLYGON (((29932.33 29879.12, 29947.32 298...
MULTIPOLYGON (((27131.28 30059.73, 27088.33 297...
MULTIPOLYGON (((26451.03 30396.46, 26440.47 303...
glimpse(mpsz)
Rows: 323
Columns: 16
$ OBJECTID   <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
$ SUBZONE_NO <int> 1, 1, 3, 8, 3, 7, 9, 2, 13, 7, 12, 6, 1, 5, 1, 1, 3, 2, 2, …
$ SUBZONE_N  <chr> "MARINA SOUTH", "PEARL'S HILL", "BOAT QUAY", "HENDERSON HIL…
$ SUBZONE_C  <chr> "MSSZ01", "OTSZ01", "SRSZ03", "BMSZ08", "BMSZ03", "BMSZ07",…
$ CA_IND     <chr> "Y", "Y", "Y", "N", "N", "N", "N", "Y", "N", "N", "N", "N",…
$ PLN_AREA_N <chr> "MARINA SOUTH", "OUTRAM", "SINGAPORE RIVER", "BUKIT MERAH",…
$ PLN_AREA_C <chr> "MS", "OT", "SR", "BM", "BM", "BM", "BM", "SR", "QT", "QT",…
$ REGION_N   <chr> "CENTRAL REGION", "CENTRAL REGION", "CENTRAL REGION", "CENT…
$ REGION_C   <chr> "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR", "CR",…
$ INC_CRC    <chr> "5ED7EB253F99252E", "8C7149B9EB32EEFC", "C35FEFF02B13E0E5",…
$ FMEL_UPD_D <date> 2014-12-05, 2014-12-05, 2014-12-05, 2014-12-05, 2014-12-05…
$ X_ADDR     <dbl> 31595.84, 28679.06, 29654.96, 26782.83, 26201.96, 25358.82,…
$ Y_ADDR     <dbl> 29220.19, 29782.05, 29974.66, 29933.77, 30005.70, 29991.38,…
$ SHAPE_Leng <dbl> 5267.381, 3506.107, 1740.926, 3313.625, 2825.594, 4428.913,…
$ SHAPE_Area <dbl> 1630379.27, 559816.25, 160807.50, 595428.89, 387429.44, 103…
$ geometry   <MULTIPOLYGON [m]> MULTIPOLYGON (((31495.56 30..., MULTIPOLYGON (…
head(mpsz, n=5)  
Simple feature collection with 5 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 25867.68 ymin: 28369.47 xmax: 32362.39 ymax: 30435.54
Projected CRS: SVY21
  OBJECTID SUBZONE_NO      SUBZONE_N SUBZONE_C CA_IND      PLN_AREA_N
1        1          1   MARINA SOUTH    MSSZ01      Y    MARINA SOUTH
2        2          1   PEARL'S HILL    OTSZ01      Y          OUTRAM
3        3          3      BOAT QUAY    SRSZ03      Y SINGAPORE RIVER
4        4          8 HENDERSON HILL    BMSZ08      N     BUKIT MERAH
5        5          3        REDHILL    BMSZ03      N     BUKIT MERAH
  PLN_AREA_C       REGION_N REGION_C          INC_CRC FMEL_UPD_D   X_ADDR
1         MS CENTRAL REGION       CR 5ED7EB253F99252E 2014-12-05 31595.84
2         OT CENTRAL REGION       CR 8C7149B9EB32EEFC 2014-12-05 28679.06
3         SR CENTRAL REGION       CR C35FEFF02B13E0E5 2014-12-05 29654.96
4         BM CENTRAL REGION       CR 3775D82C5DDBEFBD 2014-12-05 26782.83
5         BM CENTRAL REGION       CR 85D9ABEF0A40678F 2014-12-05 26201.96
    Y_ADDR SHAPE_Leng SHAPE_Area                       geometry
1 29220.19   5267.381  1630379.3 MULTIPOLYGON (((31495.56 30...
2 29782.05   3506.107   559816.2 MULTIPOLYGON (((29092.28 30...
3 29974.66   1740.926   160807.5 MULTIPOLYGON (((29932.33 29...
4 29933.77   3313.625   595428.9 MULTIPOLYGON (((27131.28 30...
5 30005.70   2825.594   387429.4 MULTIPOLYGON (((26451.03 30...
plot(mpsz)
Warning: plotting the first 9 out of 15 attributes; use max.plot = 15 to plot
all

plot(st_geometry(mpsz))

plot(mpsz["PLN_AREA_N"])

st_crs(mpsz)
Coordinate Reference System:
  User input: SVY21 
  wkt:
PROJCRS["SVY21",
    BASEGEOGCRS["SVY21[WGS84]",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]],
            ID["EPSG",6326]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["Degree",0.0174532925199433]]],
    CONVERSION["unnamed",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1,
                ID["EPSG",9001]]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1,
                ID["EPSG",9001]]]]
mpsz3414 <- st_transform(mpsz, 3414)
st_crs(mpsz3414)
Coordinate Reference System:
  User input: EPSG:3414 
  wkt:
PROJCRS["SVY21 / Singapore TM",
    BASEGEOGCRS["SVY21",
        DATUM["SVY21",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4757]],
    CONVERSION["Singapore Transverse Mercator",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",1.36666666666667,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",103.833333333333,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",28001.642,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",38744.572,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["northing (N)",north,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["easting (E)",east,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Cadastre, engineering survey, topographic mapping."],
        AREA["Singapore - onshore and offshore."],
        BBOX[1.13,103.59,1.47,104.07]],
    ID["EPSG",3414]]
preschool3414 <- st_transform(preschool, 
                              crs = 3414)
listings <- read_csv("data/aspatial/listings.csv")
Rows: 3483 Columns: 18
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr   (6): name, host_name, neighbourhood_group, neighbourhood, room_type, l...
dbl  (11): id, host_id, latitude, longitude, price, minimum_nights, number_o...
date  (1): last_review

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
list(listings)
[[1]]
# A tibble: 3,483 × 18
       id name      host_id host_name neighbourhood_group neighbourhood latitude
    <dbl> <chr>       <dbl> <chr>     <chr>               <chr>            <dbl>
 1  71609 Villa in…  367042 Belinda   East Region         Tampines          1.35
 2  71896 Home in …  367042 Belinda   East Region         Tampines          1.35
 3  71903 Home in …  367042 Belinda   East Region         Tampines          1.35
 4 275343 Rental u… 1439258 Kay       Central Region      Bukit Merah       1.29
 5 275344 Rental u… 1439258 Kay       Central Region      Bukit Merah       1.29
 6 289234 Home in …  367042 Belinda   East Region         Tampines          1.34
 7 294281 Rental u… 1521514 Elizabeth Central Region      Newton            1.31
 8 324945 Rental u… 1439258 Kay       Central Region      Bukit Merah       1.29
 9 330095 Rental u… 1439258 Kay       Central Region      Bukit Merah       1.29
10 369141 Place to… 1521514 Elizabeth Central Region      Newton            1.31
# ℹ 3,473 more rows
# ℹ 11 more variables: longitude <dbl>, room_type <chr>, price <dbl>,
#   minimum_nights <dbl>, number_of_reviews <dbl>, last_review <date>,
#   reviews_per_month <dbl>, calculated_host_listings_count <dbl>,
#   availability_365 <dbl>, number_of_reviews_ltm <dbl>, license <chr>
listings_sf <- st_as_sf(listings, coords = c("longitude", "latitude"), crs=4326) %>% st_transform(crs = 3414)
glimpse(listings_sf)
Rows: 3,483
Columns: 17
$ id                             <dbl> 71609, 71896, 71903, 275343, 275344, 28…
$ name                           <chr> "Villa in Singapore · ★4.44 · 2 bedroom…
$ host_id                        <dbl> 367042, 367042, 367042, 1439258, 143925…
$ host_name                      <chr> "Belinda", "Belinda", "Belinda", "Kay",…
$ neighbourhood_group            <chr> "East Region", "East Region", "East Reg…
$ neighbourhood                  <chr> "Tampines", "Tampines", "Tampines", "Bu…
$ room_type                      <chr> "Private room", "Private room", "Privat…
$ price                          <dbl> 150, 80, 80, 55, 69, 220, 85, 75, 45, 7…
$ minimum_nights                 <dbl> 92, 92, 92, 60, 60, 92, 92, 60, 60, 92,…
$ number_of_reviews              <dbl> 20, 24, 47, 22, 17, 12, 133, 18, 6, 81,…
$ last_review                    <date> 2020-01-17, 2019-10-13, 2020-01-09, 20…
$ reviews_per_month              <dbl> 0.14, 0.16, 0.31, 0.17, 0.12, 0.09, 0.9…
$ calculated_host_listings_count <dbl> 5, 5, 5, 52, 52, 5, 7, 52, 52, 7, 7, 1,…
$ availability_365               <dbl> 89, 89, 89, 275, 274, 89, 365, 365, 365…
$ number_of_reviews_ltm          <dbl> 0, 0, 0, 0, 3, 0, 0, 1, 3, 0, 0, 0, 0, …
$ license                        <chr> NA, NA, NA, "S0399", "S0399", NA, NA, "…
$ geometry                       <POINT [m]> POINT (41972.5 36390.05), POINT (…
buffer_cycling <- st_buffer(cyclingpath, dist=5, nQuadSegs = 30)
buffer_cycling$AREA <- st_area(buffer_cycling)
sum(buffer_cycling$AREA)
1774367 [m^2]
mpsz3414$`PreSch Count`<- lengths(st_intersects(mpsz3414, preschool3414))
summary(mpsz3414$`PreSch Count`)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    0.00    4.00    7.09   10.00   72.00 
top_n(mpsz3414, 1, `PreSch Count`)
Simple feature collection with 1 feature and 16 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 39655.33 ymin: 35966 xmax: 42940.57 ymax: 38622.37
Projected CRS: SVY21 / Singapore TM
  OBJECTID SUBZONE_NO     SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N PLN_AREA_C
1      189          2 TAMPINES EAST    TMSZ02      N   TAMPINES         TM
     REGION_N REGION_C          INC_CRC FMEL_UPD_D   X_ADDR   Y_ADDR SHAPE_Leng
1 EAST REGION       ER 21658EAAF84F4D8D 2014-12-05 41122.55 37392.39   10180.62
  SHAPE_Area                       geometry PreSch Count
1    4339824 MULTIPOLYGON (((42196.76 38...           72
mpsz3414$Area <- mpsz3414 %>%
  st_area()
mpsz3414 <- mpsz3414 %>%
  mutate(`PreSch Density` = `PreSch Count`/Area * 1000000)
hist(mpsz3414$`PreSch Density`)

ggplot(data=mpsz3414, aes(x= as.numeric(`PreSch Density`)))+ geom_histogram(bins=20, color="black", fill="light blue") + labs(title = "Are pre-school even distributed in Singapore?", subtitle= "There are many planning sub-zones with a single pre-school, on the other hand, nthere are two planning sub-zones with at least 20 pre-schools", x = "Pre-school density (per km sq)", y = "Frequency")

ggplot(data=mpsz3414, 
       aes(y = `PreSch Count`, 
           x= as.numeric(`PreSch Density`)))+
  geom_point(color="black", 
             fill="light blue") +
  xlim(0, 40) +
  ylim(0, 40) +
  labs(title = "",
      x = "Pre-school density (per km sq)",
      y = "Pre-school count")
Warning: Removed 2 rows containing missing values (`geom_point()`).